Background and Aim: In the era of deep learning, numerous models have emerged in the literature and various application domains. Transformer architectures, particularly, have gained popularity in deep learning, with diverse transformer-based computer vision algorithms. Attention convolutional neural networks (CNNs) have been introduced to enhance image classification capabilities. In this context, we propose a novel attention convolutional model with the primary objective of detecting bipolar disorder using optical coherence tomography (OCT) images. Materials and Methods: To facilitate our study, we curated a unique OCT image dataset, initially comprising two distinct cases. For the development of an automated OCT image detection system, we introduce a new attention convolutional neural network named “TurkerNeXt”. This proposed Attention TurkerNeXt encompasses four key modules: (i) the patchify stem block, (ii) the Attention TurkerNeXt block, (iii) the patchify downsampling block, and (iv) the output block. In line with the swin transformer, we employed a patchify operation in this study. The design of the attention block, Attention TurkerNeXt, draws inspiration from ConvNeXt, with an added shortcut operation to mitigate the vanishing gradient problem. The overall architecture is influenced by ResNet18. Results: The dataset comprises two distinctive cases: (i) top to bottom and (ii) left to right. Each case contains 987 training and 328 test images. Our newly proposed Attention TurkerNeXt achieved 100% test and validation accuracies for both cases. Conclusions: We curated a novel OCT dataset and introduced a new CNN, named TurkerNeXt in this research. Based on the research findings and classification results, our proposed TurkerNeXt model demonstrated excellent classification performance. This investigation distinctly underscores the potential of OCT images as a biomarker for bipolar disorder.

Purpose: Acute central serous chorioretinopathy (ACSCR) is a condition characterized by decreased visual acuity, macular thickening, and edema under the retinal layer. Although the underlying mechanisms of the disease are not fully understood, oxidative stress is considered to be a critical risk factor. The aim of this study was to shed light on the pathophysiology of ACSCR by investigating the levels of circulating trimethylamine N-oxide (TMAO), phoenixin (PNX), alarin (ALA), and spexin (SPX) molecules in ACSCR patients. Methods: The study included 30 ACSCR patients and 30 healthy individuals as controls. ACSCR was diagnosed using optical coherence tomography (OCT) imaging. Five mL blood samples were collected from all participants following overnight fasting. The levels of TMAO, PNX, ALA, and SPX in the blood samples were measured using the ELISA method. Results: Visual acuity was found to be significantly reduced in ACSCR patients compared to the control group (<0.05), while macular thickness was increased (<0.05). Furthermore, TMAO, PNX, and ALA levels were significantly higher in ACSCR patients (<0.05), while SPX levels were significantly lower compared to the control group (<0.05). In ACSCR patients, there was a positive correlation between macular thickness and TMAO, PNX, and ALA; there was, however, a negative correlation with SPX. Additionally, visual acuity was negatively correlated with TMAO, PNX, and ALA, while SPX levels decreased as visual acuity decreased. Conclusions: These results demonstrate a correlation between the TMAO, PNX, ALA, and SPX levels of ACSCR patients and their visual acuity and macular thickness. Given the role of these molecules in ACSCR’s pathophysiology, they hold promise as potential diagnostic, therapeutic, and follow-up markers in the future.